Three-dimensional multimode medical image automatic registration method based on mutual information and image segmentation

Abstract

The invention provides a three-dimensional multimode medical image automatic registration method based on mutual information and image segmentation. The method comprises the following steps that stepS1, preprocessing is performed by using a threshold method and a mathematical morphology method; step S2, segmentation is performed by using the k-means method; step S3, the optimal registration parameter based on the mutual information is obtained through iteration by using the optimization algorithm; step S4, an original reference image and a floating image are superposed; step S5, the grayscalehistogram of the reference image A through image segmentation preprocessing is calculated, and the pixels having the same grayscale value are arranged in one group; step S6, the registration parameter is initialized, and the initial values of six parameters are set as zero; and step S7, linear interpolation is performed on the floating image B by using the registration parameter to generate the changed floating image, and the zero value is assigned to the pixel points mapped to the floating image outside the reference image in the iteration process. The method is high in accuracy, high in robustness and high in performance.

Description

technical field [0001] The invention relates to the fields of computer image processing and medical image fusion, and relates to a three-dimensional multimodal medical image automatic registration method based on mutual information and image segmentation. Background technique [0002] Multimodal medical image registration is the key technology of medical image fusion. Only when medical images of different modalities are accurately registered can the fusion of image information be meaningful. [0003] The similarity index of image registration is used to evaluate the coincidence degree between the reference image and the transformed image to be registered. When the two images are fully registered, the similarity index should reach the maximum value. Since the similarity index is a function of registration transformation parameters, finding the best registration parameters is equivalent to a function optimization problem. The quality of the similarity index determines the rob...

AI Technical Summary

Problems solved by technology

[0008] In order to overcome the deficiencies of low precision, low robustness, and poor performance of existing 3D multimodal medical image registration methods, the present in

Images